1,721,034 research outputs found
Discussion of the paper "Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings", by W.Ehm, T.Gneiting, A.Jordan and F.Kruger, J.R. Statist. Soc. B, 78,3, 505-562, 2016
No full textEstimation of Spatial Processes Using Local Scoring Rules
No full textWe display pseudo-likelihood as a special case of a general estimation technique based on proper scoring rules. Such a rule supplies an unbiased estimating equation for any statistical model, and this can be extended to allow for missing data. When the scoring rule has a simple local structure, as in many spatial models, the need to compute problematic normalising constants is avoided. We illustrate the approach through an analysis of data on disease in bell pepper plants
Modelling spatial hospital recruitment via integrated nested Laplace approximations
No full textWe propose different spatial models to study hospital recruitment, including some potentially explicative variables. Data analysed
concern the hospital recruitment of the Haute Alsace a region in the north-east of France. Spatial models can be employed to show
current patterns of healthcare utilization and to monitor changes in primary care access. Interest is on the distribution per geographical unit of the ratio between the number of patients living in this geographical unit and the population in the same unit. Models considered are within the framework of Bayesian latent Gaussian models. We assume that our response variable, the
number of patients, follows, independently, a binomial distribution, with logit link, whose parameters are the population in each geographical unit and the corresponding risk. A flexible geoaddittive predictor is considered. To approximate posterior marginals, we use integrated nested Laplace approximations (INLA), recently proposed for approximate Bayesian inference in latent Gaussian models. Model comparisons are assessed using Deviance Information Criterion
A Measure of Local Sensitivity for Proper Scoring Rules in a Bayesian Setting
No full textSuppose to express the uncertainty about an unobserved quantity by quoting a distribution over , after which Nature reveals the value of . A {\em Scoring Rule} e provides a way of judging the quality of a quoted probability distribution for in the light of its outcome . It is called proper if honesty is your best policy, i.e. when you believe X has distribution P in M, your expected score is optimized by the choice Q=P. Every statistical decision problem induces a proper scoring rule.
In this work we propose a general definition of local sensitivity index for Proper Scoring Rules from a Bayesian decision point of view. We show as this new index is an intrinsic characteristic of the class M
Modelling Space-time variation of cancer incidence data: a case study
No full textCancer incidence data are typically available as rates or counts for contiguous geographical regions and are
collected over time. Recent methodological developments have moved in the direction of univariate space-time
modeling of incidence data especially in a Bayesian context. Based on an example of data on cancer incidence
collected between 1988 and 2005 in a specific area of France, this work describes an approach to analyze the
space-time evolution of the disease taking into account also of possible non linear effects of other covariates.
For this purpose, we consider Generalized Additive Mixed Models (GAMMs) with a Poisson response. The proposed method allows to incorporate a wide
range of correlation structures. Besides one dimensional smooth functions accounting for non-linear effects
of covariates, the space-time interaction can be modeled using scale invariant tensor product smooths, where
the smoothness parameter is estimated and does not depend on the different scales of the covariate axes.
Another possibility investigated to account for space-time dependency is to use varying coefficient models. In
such case, to explore spatio-temporal patterns, analyzes focused on six time periods, each 3 years in length,
between 1988 and 2005. For model implementations we use the R package mgc
A generalization of the skew-normal distribution: the beta skew-normal
No full textWe consider a new generalization of the skew-normal distribution introduced by
Azzalini (1985). We denote this distribution Beta skew-normal (BSN) since it is a
special case of the Beta generated distribution (Jones (2004)). Some properties of
the BSN are studied. We pay attention to some generalizations of the skew-normal
distribution (Bahrami et al. (2009), Shara and Behboodian (2008), Yadegari et al.
(2008)) and to their relations with the BSN
An alternative mathematical foundation for Statistics
No full textWe use a version of Non-standard Analysis with a double scale of order of magnitude
to develop an alternative foundation for Statistics. The corresponding theory is intermediate
between Statistics and Probability Theory and we view it as a link between Frequency Statistics
and Probability
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